☁️ Backend Infrastructure & DevOps

Current setup: 12 EC2 instances manually managed, inconsistent deployments, no auto-scaling, manual health checks. Plan: migrate to EKS (AWS Kubernetes). Benefits: 1) Auto-scaling based on CPU/memory/custom metrics 2) Zero-downtime rolling updates 3) Self-healing (pod restarts) 4) Resource optimization (bin packing) 5) Standardized deployments via Helm charts. Phase 1: Setup EKS cluster with 3 node groups (API, workers, monitoring). Phase 2: Create Dockerfiles and Helm charts for all services. Phase 3: Setup CI/CD to deploy to K8s. Phase 4: Migrate traffic gradually (canary deployment). Challenges: learning curve, cost optimization, persistent storage, networking complexity. Est: 21 story points. Training workshops required.

Migrate from EC2 to Kubernetes for better container orchestration

Currently single region (us-east-1), vulnerable to regional outages. AWS us-east-1 outage in Dec 2024 took us down for 6 hours. Plan: active-passive multi-region setup with us-west-2 as failover. Requirements: 1) Database cross-region replication (RDS read replica in west) 2) S3 cross-region replication for assets 3) DNS failover with Route53 health checks 4) Automated failover process (< 5 min RTO) 5) Data consistency monitoring. Challenges: replication lag, split-brain scenarios, cost (double infrastructure), testing failover. Implement: Route53 health checks, automated failover scripts, monitoring for replication lag. Test: quarterly DR drills. Cost estimate: +70% infrastructure. Estimated 21 story points. Business continuity critical.

Setup multi-region deployment for disaster recovery

Logs scattered across 12 EC2 instances, impossible to debug issues, no log retention policy. Implement ELK (Elasticsearch, Logstash, Kibana) for centralized logging. Requirements: 1) Ship logs from all servers to Elasticsearch 2) Parse logs with Logstash (JSON format) 3) Create Kibana dashboards for common queries 4) Alert on error patterns 5) 30-day retention, archive to S3. Use Filebeat for log shipping. Structure logs: timestamp, level, service, message, context, trace_id. Create dashboards: error rate by service, slow requests, failed auth attempts, API endpoint performance. Setup alerts: spike in errors, critical errors. Alternative: consider AWS CloudWatch Logs Insights or Datadog. 8 story points.

Setup centralized logging with ELK stack

Current backups: manual exports, inconsistent, last backup 2 weeks old, no test of restore process. Critical risk: data loss. Implement: 1) RDS automated backups enabled (retention 35 days) 2) Transaction log shipping for PITR 3) Cross-region backup copy 4) Encrypted backups 5) Automated restore testing monthly 6) Backup monitoring and alerts. Document: restore procedures for different scenarios (single table, full DB, point-in-time). Setup: backup window during low traffic (3-4am), snapshot before major changes, backup validation checks. Test: restore to new instance, verify data integrity. Compliance requirement for SOC 2. Estimate cost: +$500/month for storage. 5 story points. P0 priority.

Setup automated database backups with point-in-time recovery

Traffic varies 10x between peak (2-4pm) and off-peak (2-6am) but infrastructure static. Wasting money on idle servers, occasionally overwhelmed during peaks. Implement auto-scaling: 1) Horizontal pod autoscaling (HPA) in Kubernetes based on CPU/memory 2) Custom metrics scaling (request queue length, response time) 3) Scheduled scaling (scale up before known peaks) 4) Predictive scaling based on historical patterns. Metrics: scale up at 70% CPU, scale down at 30%, min 3 pods, max 20 pods, cooldown periods. Also implement: pod disruption budgets, graceful shutdown, connection draining. Monitor: scaling events, cost savings. Expected: 40% cost reduction off-peak, better performance during peaks. 8 story points.

Setup auto-scaling policies based on traffic patterns

Database connections are major bottleneck. Current: creating new connection for each request (20-50ms overhead), no connection reuse, connection exhaustion during peaks. Implement PgBouncer for connection pooling. Configuration: pool mode transaction, pool size 100, reserve connections 10, max client connections 1000. Expected improvement: 40-60ms reduction in query latency, handle 10x more concurrent requests. Also optimize queries: 1) Add missing indexes (identified 23 slow queries) 2) Rewrite N+1 queries to use joins 3) Implement query result caching 4) Add EXPLAIN ANALYZE to slow query log 5) Set query timeouts. Monitor: connection pool utilization, query performance, cache hit rate. 8 story points.

Implement database connection pooling and query optimization

All infrastructure manually created via AWS Console - no version control, no reproducibility, disaster recovery impossible. Goal: codify entire infrastructure in Terraform. Scope: VPC, subnets, security groups, RDS, ElastiCache, S3, CloudFront, Load Balancers, EKS cluster, IAM roles. Benefits: 1) Version controlled infrastructure 2) Reproducible environments (dev/staging/prod identical) 3) Easy disaster recovery 4) Code review for infrastructure changes 5) Terraform Cloud for state management. Start: import existing resources, then manage via Terraform. Modules: network, database, compute, storage, monitoring. Include: remote state in S3, state locking with DynamoDB, workspaces for environments. Document: Terraform workflow, module usage, common commands. 13 story points.

Implement infrastructure as code using Terraform

Current Docker images: 2.4GB for Node app (should be <500MB), builds take 12 minutes (should be <3min). Issues: 1) Not using multi-stage builds 2) Installing dev dependencies in production 3) No layer caching strategy 4) Copying entire repo including node_modules 5) Large base images (node:latest). Optimizations: 1) Multi-stage builds (build stage + production stage) 2) Use Alpine base images 3) Copy package files first for layer caching 4) .dockerignore file 5) Production-only dependencies 6) Combine RUN commands. Target: <300MB images, <2min builds. Benefits: faster deployments, reduced storage costs, quicker instance startup. Add: Docker security scanning, vulnerability checks. 5 story points. Quick win.

Optimize Docker images to reduce build time and image size

Current deployments: replace running containers causing 2-3min downtime. Users get 502 errors during deploy. Implement blue-green deployment: maintain two identical production environments, switch traffic atomically. Process: 1) Deploy new version to 'green' environment 2) Run smoke tests on green 3) Switch load balancer to green (atomic) 4) Monitor for issues 5) Keep blue as instant rollback option 6) After validation, blue becomes next green. Benefits: zero-downtime deployments, instant rollback, test production environment before traffic. Implementation: ALB target groups, weighted routing, automated smoke tests, automated rollback on errors. Add: deployment tracking, deployment notifications to Slack. 8 story points. Required before SLA commitments.

Implement blue-green deployment strategy

Secrets currently in: .env files committed to git (!) environment variables, hardcoded in code. Security nightmare. Implement Vault for secrets management. Migration: 1) Setup Vault cluster (HA) 2) Migrate all secrets to Vault 3) Application integration (Vault SDK) 4) Dynamic database credentials 5) Secret rotation policies 6) Audit logging. Secrets to migrate: DB passwords, API keys, JWT secrets, AWS credentials, OAuth secrets, encryption keys. Features: role-based access, secret versioning, TTL for secrets, automated rotation. Remove secrets from git history (BFG Repo-Cleaner). Security audit required. Team training on Vault usage. 13 story points. Critical security issue.

Implement secrets management with HashiCorp Vault

Currently using Let's Encrypt with manual renewal process, certs expire every 90 days, already had 2 expired cert incidents causing site outage. Migrate to AWS Certificate Manager (ACM). Benefits: 1) Free SSL certs 2) Automatic renewal 3) Easy integration with ALB/CloudFront 4) Wildcard cert support. Migration: 1) Request certs in ACM 2) Validate domain ownership (DNS) 3) Update ALB to use ACM certs 4) Setup cert expiration monitoring (backup) 5) Remove manual cert renewal process. Also: implement CAA DNS records, enable perfect forward secrecy, upgrade to TLS 1.3 only. Security scan with SSL Labs (target: A+ rating). 3 story points. Quick win, eliminates toil.

Migrate SSL certificate management to AWS ACM with auto-renewal

Current: CD for staging only, production deploys manual. Goal: full CD to production with safety mechanisms. Pipeline: 1) PR merged → automated tests → staging deploy → smoke tests → production canary (5% traffic) → monitor (10min) → gradual rollout (25%, 50%, 100%) or automatic rollback on errors. Rollback triggers: error rate >1%, latency P95 >500ms, HTTP 5xx >0.5%. Implementation: GitHub Actions, custom health check scripts, Slack notifications, deployment tracking in Datadog. Safeguards: deployment windows (no deploys Fri/weekend), feature flags for risky changes, on-call acknowledgment. Document: deployment process, rollback procedures, incident response. 13 story points. Increases deployment velocity.

Setup Continuous Deployment with automatic rollback on errors

Single PostgreSQL database shared by all services, schema sprawl (300+ tables), coupling between services, migration nightmare. Move to database-per-service pattern for microservices. Plan: 1) Identify service boundaries and data ownership 2) Create separate RDS instances for each service 3) Migrate data with zero downtime 4) Update service code to use new databases 5) Handle cross-service queries. Challenges: breaking foreign keys across services, data consistency, increased cost, operational complexity. Pattern: use APIs for cross-service data access, eventual consistency with events. Start with User Service, then Payment Service. Estimated: 21 story points over 2 sprints. Requires architect approval.

Migrate from monolithic RDS to database per service

Current: SSH directly to production servers from anywhere, overly permissive security groups, no audit log of who accessed what. Security risk. Implement: 1) VPN for secure access to private network 2) Bastion host as jump box for SSH access 3) Restrict SSH to only from bastion 4) MFA for bastion access 5) Session recording and audit logs. Setup: AWS Client VPN or self-hosted WireGuard, bastion in public subnet, all other instances in private subnets, NAT gateway for egress. IAM roles for access control, temporary SSH keys (no permanent keys), automatically rotate bastion credentials. Audit log all sessions with timestamps, user, commands. Review access logs quarterly. 8 story points. Security hardening.

Setup VPN and bastion hosts for secure infrastructure access

Never tested system resilience, don't know how system behaves under failure conditions. Implement chaos engineering to find weaknesses. Start with staging: 1) Random pod termination 2) Network latency injection 3) Database connection failures 4) API dependency failures 5) Resource exhaustion (CPU/memory). Use tools: Chaos Mesh or AWS Fault Injection Simulator. Tests: verify circuit breakers work, graceful degradation, proper error messages, monitoring/alerting triggers, recovery time. Create: hypothesis for each test, measure impact, document findings, create action items for issues found. Schedule: bi-weekly chaos testing in staging, quarterly in production (during low traffic). GameDays for team training. 8 story points. Increases reliability.

Inbox

☁️ Backend Infrastructure & DevOps

Backlog

Planning

In Progress

Review

Deployed